Smart Container Yard Management | Savvycom AI Logistics Detailed Case Study

Smart Management for a Global Logistics Enterprise

Note: This is a real case successfully delivered by Savvycom; however, we cannot disclose our client's name to ensure privacy and data confidentiality.

INDUSTRY:

Manufacturing

SERVICES/SOLUTIONS:

Document AI (OCR & NLP), AI Computer Vision

DURATION:

9 months

COUNTRY:

Vietnam

Overview

A global logistics provider operating high-volume container yards faced growing operational challenges as daily container movements increased. To address limited visibility and manual yard processes, an AI-powered Yard Management System (YMS) was implemented to enable real-time container tracking, optimize yard operations, and reduce handling delays across multiple sites in Vietnam and global locations.

About Client

The client is a global logistics provider managing large-scale logistics yards with 400–500 container movements per day and over 500 containers stored simultaneously at each site. Their operations required high accuracy, fast container retrieval, and real-time operational visibility to support complex, large-volume yard activities.

Changllenges

The client encountered several operational challenges as yard volume and complexity increased:

Limited real-time visibility made it difficult to locate containers quickly, causing delays in yard operations.
Manual yard records led to inconsistencies, operational errors, and misplaced containers.
Internal container movements were not fully tracked, reducing traceability and control.
High daily container volume created bottlenecks, prolonged handling cycles, and increased operational pressure on yard teams.

Solutions

To address these challenges, an AI-driven approach was applied to redesign yard operations and replace manual processes with intelligent automation. The solution combined real-time tracking, visual data, and operational dashboards to improve accuracy, speed, and scalability across yard operations.

AI Camera Integration: Automated recognition of container IDs, trailer IDs, and truck license plates at entry and exit points.
Real-time Container Tracking: AI-powered object detection and tracking to monitor container movements continuously across the yard.
2D Digital Yard Mapping: Real-time visualization of container positions and statuses for improved visibility and coordination.
Digital Move Orders: AI-validated container placement to ensure accuracy based on camera detection and system logic.
Operational Dashboards: Instant container search, dwell time monitoring, and actionable operational insights for yard teams.

Results

Following implementation, container tracking accuracy improved significantly, and container search time was reduced. Yard operations became more streamlined, manual errors decreased, and overall productivity increased as teams gained better visibility and control over daily operations.

60% reduction in retrieval time: Improved real-time visibility and tracking eliminated delays in locating containers across yard operations.
35–40% increase in operational productivity: Streamlined workflows and better container control enabled teams to operate more efficiently at scale.
95% accuracy in container placement and movement tracking: AI-driven monitoring significantly improved tracking precision and reduced operational uncertainty.
Significantly reduced manual errors and container misplacement incidents.

Tech Stacks

AI & Computer Vision: YOLOv8 with DeepSORT for real-time container tracking, combined with PaddleOCR for accurate container and vehicle ID recognition.
Backend & Data: Python and .NET services with PostgreSQL and MinIO for reliable data processing and storage.
Streaming & Integration: Kafka-powered event streaming with RESTful APIs for real-time system communication.
Cloud Platform: Deployed on Microsoft Azure to ensure scalable, secure, multi-site yard operations.